A New Approach to Linear/Nonlinear Distributed Fusion Estimation Problem

Disturbance noises are always bounded in a practical system, while fusion estimation is to best utilize multiple sensor data containing noises for the purpose of estimating a quantity--a parameter or process. However, few results are focused on the information fusion estimation problem under bounded noises. In this paper, we study the distributed fusion estimation problem for linear time-varying systems and nonlinear systems with bounded noises, where the addressed noises do not provide any statistical information, and are unknown but bounded. When considering linear time-varying fusion systems with bounded noises, a new local Kalman-like estimator is designed such that the square error of the estimator is bounded as time goes to $\infty$. A novel constructive method is proposed to find an upper bound of fusion estimation error, then a convex optimization problem on the design of an optimal weighting fusion criterion is established in terms of linear matrix inequalities, which can be solved by standard software packages. Furthermore, according to the design method of linear time-varying fusion systems, each local nonlinear estimator is derived for nonlinear systems with bounded noises by using Taylor series expansion, and a corresponding distributed fusion criterion is obtained by solving a convex optimization problem. Finally, target tracking system and localization of a mobile robot are given to show the advantages and effectiveness of the proposed methods.Comment: 9 pages, 3 figure

Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks

Recent advances in electronics are enabling substantial processing to be performed at each node (robots, sensors) of a networked system. Local processing enables data compression and may mitigate measurement noise, but it is still slower compared to a central computer (it entails a larger computational delay). However, while nodes can process the data in parallel, the centralized computational is sequential in nature. On the other hand, if a node sends raw data to a central computer for processing, it incurs communication delay. This leads to a fundamental communication-computation trade-off, where each node has to decide on the optimal amount of preprocessing in order to maximize the network performance. We consider a network in charge of estimating the state of a dynamical system and provide three contributions. First, we provide a rigorous problem formulation for optimal real-time estimation in processing networks in the presence of delays. Second, we show that, in the case of a homogeneous network (where all sensors have the same computation) that monitors a continuous-time scalar linear system, the optimal amount of local preprocessing maximizing the network estimation performance can be computed analytically. Third, we consider the realistic case of a heterogeneous network monitoring a discrete-time multi-variate linear system and provide algorithms to decide on suitable preprocessing at each node, and to select a sensor subset when computational constraints make using all sensors suboptimal. Numerical simulations show that selecting the sensors is crucial. Moreover, we show that if the nodes apply the preprocessing policy suggested by our algorithms, they can largely improve the network estimation performance.Comment: 15 pages, 16 figures. Accepted journal versio

Information fusion architectures for security and resource management in cyber physical systems

Data acquisition through sensors is very crucial in determining the operability of the observed physical entity. Cyber Physical Systems (CPSs) are an example of distributed systems where sensors embedded into the physical system are used in sensing and data acquisition. CPSs are a collaboration between the physical and the computational cyber components. The control decisions sent back to the actuators on the physical components from the computational cyber components closes the feedback loop of the CPS. Since, this feedback is solely based on the data collected through the embedded sensors, information acquisition from the data plays an extremely vital role in determining the operational stability of the CPS. Data collection process may be hindered by disturbances such as system faults, noise and security attacks. Hence, simple data acquisition techniques will not suffice as accurate system representation cannot be obtained. Therefore, more powerful methods of inferring information from collected data such as Information Fusion have to be used. Information fusion is analogous to the cognitive process used by humans to integrate data continuously from their senses to make inferences about their environment. Data from the sensors is combined using techniques drawn from several disciplines such as Adaptive Filtering, Machine Learning and Pattern Recognition. Decisions made from such combination of data form the crux of information fusion and differentiates it from a flat structured data aggregation. In this dissertation, multi-layered information fusion models are used to develop automated decision making architectures to service security and resource management requirements in Cyber Physical Systems --Abstract, page iv

Trust-Based Distributed Kalman Filtering for Target Tracking under Malicious Cyber Attacks

As one of the widely used applications in wireless sensor networks, target tracking has attracted considerable attention. Although many tracking techniques have been developed, it is still a challenging problem if the network is under cyber attacks. Inaccurate or false information is maliciously broadcast by the compromised nodes to their neighbors. They are likely to threaten the security of the system and result in performance deterioration. In this paper, a distributed Kalman filtering technique with trust-based dynamic combination strategy is developed to improve resilience against cyber attacks. Furthermore, it is efficient in terms of communication load, only local instantaneous estimates are exchanged with the neighboring nodes. Numerical results are provided to evaluate the performance of the proposed approach by considering random, false data injection and replay attacks

Cyber-Physical Security Strategies

Cyber-physical security describes the protection of systems with close relationships between computational functions and physical ones and addresses the issue of vulnerability to attack through both cyber and physical avenues. This describes systems in a wide variety of functions, many crucial to the function of modern society, making their security of paramount importance. The development of secure system design and attack detection strategies for each potential avenue of attack is needed to combat malicious attacks. This thesis will provide an overview of the approaches to securing different aspect of cyber-physical systems. The cyber element can be designed to better prevent unauthorized entry and to be more robust to attack while its use is evaluated for signs of ongoing intrusion. Nodes in sensor networks can be evaluated by their claims to determine the likelihood of their honesty. Control systems can be designed to be robust in cases of the failure of one component and to detect signal insertion or replay attack. Through the application of these strategies, the safety and continued function of cyber-physical systems can be improved

Distributed estimation techniques forcyber-physical systems

Nowadays, with the increasing use of wireless networks, embedded devices and agents with processing and sensing capabilities, the development of distributed estimation techniques has become vital to monitor important variables of the system that are not directly available. Numerous distributed estimation techniques have been proposed in the literature according to the model of the system, noises and disturbances. One of the main objectives of this thesis is to search all those works that deal with distributed estimation techniques applied to cyber-physical systems, system of systems and heterogeneous systems, through using systematic review methodology. Even though systematic reviews are not the common way to survey a topic in the control community, they provide a rigorous, robust and objective formula that should not be ignored. The presented systematic review incorporates and adapts the guidelines recommended in other disciplines to the field of automation and control and presents a brief description of the different phases that constitute a systematic review. Undertaking the systematic review many gaps were discovered: it deserves to be remarked that some estimators are not applied to cyber-physical systems, such as sliding mode observers or set-membership observers. Subsequently, one of these particular techniques was chosen, set-membership estimator, to develop new applications for cyber-physical systems. This introduces the other objectives of the thesis, i.e. to present two novel formulations of distributed set-membership estimators. Both estimators use a multi-hop decomposition, so the dynamics of the system is rewritten to present a cascaded implementation of the distributed set-membership observer, decoupling the influence of the non-observable modes to the observable ones. So each agent must find a different set for each sub-space, instead of a unique set for all the states. Two different approaches have been used to address the same problem, that is, to design a guaranteed distributed estimation method for linear full-coupled systems affected by bounded disturbances, to be implemented in a set of distributed agents that need to communicate and collaborate to achieve this goal

Context-Aware Sensor Fusion For Securing Cyber-Physical Systems

The goal of this dissertation is to provide detection and estimation techniques in order to ensure the safety and security of modern Cyber-Physical Systems (CPS) even in the presence of arbitrary sensors faults and attacks. We leverage the fact that modern CPS are equipped with various sensors that provide redundant information about the system\u27s state. In such a setting, the system can limit its dependence on any individual sensor, thereby providing guarantees about its safety even in the presence of arbitrary faults and attacks. In order to address the problem of safety detection, we develop sensor fusion techniques that make use of the sensor redundancy available in modern CPS. First of all, we develop a multidimensional sensor fusion algorithm that outputs a bounded fusion set which is guaranteed to contain the true state even in the presence of attacks and faults. Furthermore, we provide two approaches for strengthening sensor fusion\u27s worst-case guarantees: 1) incorporating historical measurements as well as 2) analyzing sensor transmission schedules (e.g., in a time-triggered system using a shared bus) in order to minimize the attacker\u27s available information and impact on the system. In addition, we modify the sensor fusion algorithm in order to provide guarantees even when sensors might experience transient faults in addition to attacks. Finally, we develop an attack detection technique (also in the presence of transient faults) in order to discard attacked sensors. In addition to standard plant sensors, we note that modern CPS also have access to multiple environment sensors that provide information about the system\u27s context (e.g., a camera recognizing a nearby building). Since these context measurements are related to the system\u27s state, they can be used for estimation and detection purposes, similar to standard measurements. In this dissertation, we first develop a nominal context-aware filter (i.e., with no faults or attacks) for binary context measurements (e.g., a building detection). Finally, we develop a technique for incorporating context measurements into sensor fusion, thus providing guarantees about system safety even in cases where more than half of standard sensors might be under attack

Distributed estimation over a low-cost sensor network: a review of state-of-the-art

Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted

Outdated Measurements Are Still Useful For Multi-Sensor Linear Control Systems With Random Communication Delays

Linear systems are a widely used model for the control tasks of modern cyber physical systems around their stationary state(s), e.g., smart grids, remote health applications, and autonomous driving systems. Specifically, each sensor first compresses its own measurement and then sends it to the controller. Due to the inevitable random communication delay, the controller needs to decide how to fuse the received information to compute the desired control action. Suppose a fusion center has received several measurements over time. One common belief is that the control decision should be made solely based on the latest measurement of each sensor while ignoring the older/stale measurements from the same sensor. This work shows that while such a strategy is optimal in a single-sensor environment, it can be strictly suboptimal for a multi-sensor system. Namely, if one properly fuses both the latest and outdated measurements from each of the sensors, one can strictly improve the underlying control system performance. The numerical evaluation shows that even at a very low communication rate of 8 bits per measurement per sensor, the proposed scheme achieves a state variance of only 5% away from the best possible achievable L2 norm. It is 15% better than the MMSE fusion scheme using exclusively the freshest measurements (while discarding outdated ones)